Fighting Cancer with Machine learning
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Research Focus

We are building the Cancer Dependency Map: an effort to systematically identify the chemical and genetic vulnerabilities of different cancer cells, as well as their predictive biomarkers.

Precision cancer medicine

We build predictive models to predict cancer vulnerabilities from genomic profiles of tumors and cancer cell lines.

Cancer Targets Identification

We integrate functional screening and 'omics data to identify novel cancer targets as well as drugs for repurposing.

CRISPR/Cas9
screens

We develop computational methods and tools to facilitate the analysis of CRISPR screening in cancer models.

Small-molecule screens

We analyze highly-multiplexed small-molecule screening data from the PRISM platform to discover novel cancer therapeutic leads.

Our Team

Aviad Tsherniak

Founder and Scientific Advisor

Philip Montgomery

Sr Principal Software Engineer

Mike Burger

Senior Computational Associate

Neekesh Dharia

Postdoctoral Scholar

James McFarland

Group Leader

Josephine Lee

Software Engineer

Josh Dempster

Data Scientist II

Jordan Rossen

Associate Computational Biologist II

Allie Warren

Associate Computational Biologist II

Jérémie Kalfon

Associate Computational Biologist I

Andrew Tang

Sr. Visual Designer

Mustafa Kocak

Computational Scientist I

Phoebe Moh

Associate Software Engineer

Mariya Kazachkova

Associate Computational Biologist I

Vickie Wang

Associate Computational Biologist I

Josh Pan

Postdoctoral fellow

Yejia Chen

Software Engineer

Ashir Borah

Associate Computational Biologist I

Gwen Miller

Broad Cancer Scholar - Associate Computational Biologist

Andrew Boghossian

Associate Computational Biologist I

Nishant Jha

Software Engineer

Javad Noorbakhsh

Computational Scientist II

Lena Joesch-Cohen

Associate Computational Biologist I

Alumni

Han Xu

Associate Professor, MD Anderson

Robin Meyers

Graduate Student, Genetics, Stanford University

Jared Jacobsen

Studying for AI Research

Li Wang

Computational Biologist, 10X Genomics

Jordan Bryan

Graduate Student, Statistics, Duke University

Kailash Nakagawa

Undergraduate Student, Stanford University

Quinton Wessells

Graduate Student, Biomedical Informatics, Stanford University

Remi Marenco

Bioinformation Lead, Cancer Cell Line Factory

Guillaume Kugener

Medical Student, USC

Zandra Ho

Medical Student, Brown

Andy Jones

Graduate Student, Computer Science, Princeton University

CDS Outings

Join Us

Associate Computational Biologist

Apply here

As a member of the Cancer Data Science team, you'll apply machine learning to solve some of the most urgent problems in cancer research. You’ll be responsible for producing keystone datasets used by tens of thousands of researchers, developing and publishing advances in computational biology, and collaborating with some of the most skilled cancer biologists in the world to develop new technologies. Along the way, you'll cultivate your own ideas and projects with the support of a world-class computational group. Our team has backgrounds ranging from oncology to physics, cell biology to statistics, united in pursuit of a single goal: to finally transform the paradigm of cancer treatment.

Publications

Selected publications

All publications

Contact Us

James McFarland

Cancer Data Science
Broad Institute of MIT and Harvard
415 Main Street
Cambridge, MA 02142

Email: jmmcfarl at broadinstitute.org